Eric Bilinski

The LIMSI Participation in the QAst Track

In this paper, we present two different question-answering systems on speech transcripts which participated to the QAst 2007 evaluation. These two systems are based on a complete and multi-level analysis of both queries and documents. The first system uses handcrafted rules for small text fragments (snippet) selection and answer extraction. The second one replaces the handcrafting with an automatically generated research descriptor. A score based on those descriptors is used to select documents and snippets. The extraction and scoring of candidate answers is based on proximity measurements within the research descriptor elements and a number of secondary factors. The evaluation results are ranged from 17% to 39% as accuracy depending on the tasks.

The LIMSI 2006 TC-STAR EPPS Transcription Systems

This paper describes the speech recognizers developed to transcribe European Parliament Plenary Sessions (EPPS) in English and Spanish in the 2nd TC-STAR Evaluation Campaign. The speech recognizers are state-of-the-art systems using multiple decoding passes with models (lexicon, acoustic models, language models) trained for the different transcription tasks. Compared to the LIMSI TC-STAR 2005 EPPS systems, relative word error rate reductions of about 30% have been achieved on the 2006 development data. The word error rates with the LIMSI systems on the 2006 EPPS evaluation data are 8.2% for English and 7.8% for Spanish. Experiments with cross-site adaptation and system combination are also described.

The LIMSI RT07 Lecture Transcription System

A system to automatically transcribe lectures and presentations has been developed in the context of the FP6 Integrated Project Chil . In addition to the seminar data recorded by the Chil partners, widely available corpora were used to train both the acoustic and language models. Acoustic model training made use of the transcribed portion of the TED corpus of Eurospeech recordings, as well as the ICSI, ISL, and NIST meeting corpora. For language model training, text materials were extracted from a variety of on-line conference proceedings. Experimental results are reported for close-talking and far-field microphones on development and evaluation data.

The LIMSI participation in the QAst 2009 track: experimenting on answer scoring

We present in this paper the three LIMSI question-answering systems on speech transcripts which participated to the QAst 2009 evaluation. These systems are based on a complete and multi-level analysis of both queries and documents. These systems use an automatically generated research descriptor. A score based on those descriptors is used to select documents and snippets. Three different methods are tried to extract and score candidate answers, and we present in particular a tree transformation based ranking method. We participated to all the tasks and submitted 30 runs (for 24 sub-tasks). The evaluation results for manual transcripts range from 27% to 36% for accuracy depending on the task and from 20% to 29% for automatic transcripts.

The LIMSI multilingual, multitask QAst system

In this paper, we present the LIMSI question-answering system which participated to the Question Answering on speech transcripts 2008 evaluation. This systems is based on a complete and multi-level analysis of both queries and documents. It uses an automatically generated research descriptor. A score based on those descriptors is used to select documents and snippets. The extraction and scoring of candidate answers is based on proximity measurements within the research descriptor elements and a number of secondary factors. We participated to all the subtasks and submitted 18 runs (for 16 sub-tasks). The evaluation results for manual transcripts range from 31% to 45% for accuracy depending on the task and from 16 to 41% for automatic transcripts.

The LIMSI QAst systems: Comparison between human and automatic rules generation for question-answering on speech transcriptions

In this paper, we present two different question-answering systems on speech transcripts. These two systems are based on a complete and multi-level analysis of both queries and documents. The first system uses handcrafted rules for small text fragments (snippet) selection and answer extraction. The second one replaces the handcrafting with an automatically generated research descriptor. A score based on those descriptors is used to select documents and snippets. The extraction and scoring of candidate answers is based on proximity measurements within the research descriptor elements and a number of secondary factors. The preliminary results obtained on QAst (QA on speech transcripts) development data are promising ranged from 72% correct answer at 1 st rank on manually transcribed meeting data to 94% on manually transcribed lecture data.

The LIMSI RT06s lecture transcription system

This paper describes recent research carried out in the context of the FP6 Integrated Project Chil in developing a system to automatically transcribe lectures and presentations. Widely available corpora were used to train both the acoustic and language models, since only a small amount of Chil data was available for system development. Acoustic model training made use of the transcribed portion of the TED corpus of Eurospeech recordings, as well as the ICSI, ISL, and NIST meeting corpora. For language model training, text materials were extracted from a variety of on-line conference proceedings. Experimental results are reported for close-talking and far-field microphones on development and evaluation data.

Description of the PatientGenesys Dialogue System

This paper describes the work-in-progress prototype of a dialog system that simulates a virtual patient (VP) consultation. We report some challenges and difficulties that are found during its development, especially in managing the interaction and the vocabulary from the medical domain.